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We find evidence that the runs on banks and trust companies in the Panic of 1907 were linked to the Bank of England’s contractionary monetary policy actions taken in 1906 and 1907 through the medium of copper prices. Results from our vector autoregressive models and copper stockpile data support our argument that a copper commodity price channel may have been active in transmitting the Bank’s policy to the New York markets. Archival evidence suggests that the plunge in copper prices may have partially triggered both the initiation and the failure of an attempt to corner the shares of United Copper, and in turn, the bank and trust company runs related to that transaction’s failure. We suggest that the substantial short-term uncertainties accompanying the development of the copper-intensive electrical and telecommunications industries likely played a role in the plunge in copper prices. Additionally, we find evidence that the copper price transmission mechanism was also likely active in five other countries that year. While we do not argue that copper caused the 1907 crisis, we suggest that it was an active policy transmission channel amplifying the classic effect that was already spreading through the money market channel. If the bust in copper prices partially triggered the 1907 panic, then it provides additional evidence that contractionary monetary policy may have had an unintended, adverse consequence of contributing to a bank panic and, therefore, supports other recent findings that monetary policy deliberations might benefit from considering the policy impact on asset prices.

The overall objective of this chapter is measuring the effect of key economic indicators and trends on the media reputation of an emergent country. The case analyzed is that of Chile, since 1990–2015. To deal with our objective, we measured the media reputation of Chile following validated criteria by Deephouse (2000).

A regression analysis was conducted to test our hypothesis that the coefficient of media favorableness (CoMF) of a country depends on the favorable or unfavorable trend of key economic indicators of the country. The dependent variable of our model was the Chilean CoMF. Independent variables were the monthly GDP variation, the monthly unemployment rate, the monthly average of the stock exchange index, the monthly average fuel price, and the monthly average copper price (a very important commodity to Chile).

Our results demonstrate that key economic indicators have a significant positive bearing on the media reputation of an emergent country as Chile, that is, when an emergent country is doing well economically, the press with a global scope tends to improve the reputation of that country, showing a more favorable image about it. In consequence, our hypothesis is supported. In the case of an emergent and small Western country as Chile, the price of commodities appears as the most important predictive indicator of its favorable or unfavorable country reputation. Other implications are discussed in the study.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.

The World Bank report Changing Wealth of Nations 2018 is only the most recent reminder of how much poorer Africa is becoming, losing more than US$100 billion annually from minerals, oil, and gas extraction, according to (quite conservatively framed) environmentally sensitive adjustments of wealth. With popular opposition to socioeconomic, political, and ecological abuses rising rapidly in Africa, a robust debate may be useful: between those practicing anti-extractivist resistance, and those technocrats in states and international agencies who promote “ecological modernization” strategies. The latter typically aim to generate full-cost environmental accounting, and to do so they typically utilize market-related techniques to value, measure, and price nature. Between the grassroots and technocratic standpoints, a layer of Non-Governmental Organizations (NGOs) do not yet appear capable of grappling with anti-extractivist politics with either sufficient intellectual tools or political courage. They instead revert to easier terrains within ecological modernization: revenue transparency, project damage mitigation, Free Prior and Informed Consent (community consultation and permission), and other assimilationist reforms. More attention to political-economic and political-ecological trends – including the end of the commodity super-cycle, worsening climate change, financial turbulence and the potential end of a 40-year long globalization process – might assist anti-extractivist activists and NGO reformers alike. Both could then gravitate to broader, more effective ways of conceptualizing extraction and unequal ecological exchange, especially in Africa’s hardest hit and most extreme sites of devastation.

In October 2020, Zambia failed to make a $42.5 million interest payment on $1 billion in Eurobonds maturing in 2024, becoming the first African country to default on its debt obligations in the aftermath of COVID-19. Zambia's default highlights the fragmented nature of governance in sovereign debt markets. The Zambian default also underscores the continuing impact of colonial hangover in former colonies in Africa. Fragmented governance and colonial overhang create incentives for both debtors and creditors that contribute to cycles of sovereign debt. These cycles of debt pose a particular hazard to residents within countries that issue such debt. In African contexts, this has led to flows of funds for debt repayment that may significantly jeopardize the well-being of people who are already poor. Zambia's default also reflects the increasing need of African countries to navigate among different external actors, particularly China, which has given loans throughout Africa for varied projects, including infrastructure lending as part of its Belt and Road Initiative. The Zambian default draws attention to the significant amount of Eurobond debt African countries have incurred in recent years and the burdens that such debt may impose. The circumstances of Zambia's default, as well as recent disputes about external debt in Mozambique, reflect continuing issues about transparency and public scrutiny of sovereign debt transactions and the broader societal impact of debt internally within African countries and in relations between African countries and varied external powers.

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These chapters construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coefficient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call “Google probabilities”: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we find DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS methods.